Comparative evaluation of bibliometric content networks by tomographic content analysis: An application to Parkinson's disease

Keeheon Lee, Su Yeon Kim, Erin Hea Jin Kim, Min Song

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

To understand the current state of a discipline and to discover new knowledge of a certain theme, one builds bibliometric content networks based on the present knowledge entities. However, such networks can vary according to the collection of data sets relevant to the theme by querying knowledge entities. In this study we classify three different bibliometric content networks. The primary bibliometric network is based on knowledge entities relevant to a keyword of the theme, the secondary network is based on entities associated with the lower concepts of the keyword, and the tertiary network is based on entities influenced by the theme. To explore the content and properties of these networks, we propose a tomographic content analysis that takes a slice-and-dice approach to analyzing the networks. Our findings indicate that the primary network is best suited to understanding the current knowledge on a certain topic, whereas the secondary network is good at discovering new knowledge across fields associated with the topic, and the tertiary network is appropriate for outlining the current knowledge of the topic and relevant studies.

Original languageEnglish
Pages (from-to)1295-1307
Number of pages13
JournalJournal of the Association for Information Science and Technology
Volume68
Issue number5
DOIs
Publication statusPublished - 2017 May 1

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content analysis
Disease
evaluation
Evaluation
Bibliometrics
Content analysis
Parkinson's disease
knowledge
present

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management
  • Library and Information Sciences

Cite this

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